
# Importing function:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
pd.set_option('display.max_rows', 1000)
from collections import defaultdict

# NLP library
from nltk.corpus import wordnet
from nltk.corpus import stopwords
from nltk.tokenize import WhitespaceTokenizer
from nltk.stem import WordNetLemmatizer
from nltk import pos_tag
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk  
from gensim.test.utils import common_texts
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from wordcloud import WordCloud
import string
import plotly.graph_objs as go
from plotly import subplots
import plotly.offline as py

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc, roc_auc_score


# nltk.download('vader_lexicon')
# nltk.download('stopwords')
# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')


# reading input data
reviews_df = pd.read_csv("H_Reviews.csv")
reviews_df.head()


# append the positive and negative text reviews
reviews_df["review"] = reviews_df["Negative_Review"] + reviews_df["Positive_Review"]
# create the label based on reviews from customers
reviews_df["is_bad_review"] = reviews_df["Reviewer_Score"].apply(lambda x: 1 if x < 5 else 0)
# select only relevant columns
reviews_df = reviews_df[["review", "is_bad_review"]]
reviews_df.head()


sns.countplot(data = reviews_df, x= 'is_bad_review')

<AxesSubplot:xlabel='is_bad_review', ylabel='count'>


# Downsample data, here we are taking 0.1% of orignal dataset
reviews_df = reviews_df.sample(frac = 0.1, replace = False, random_state=42)
reviews_df.shape

(51574, 2)


reviews_df["is_bad_review"].value_counts(normalize = True)

0    0.956761
1    0.043239
Name: is_bad_review, dtype: float64


# Removing 'No Negative' or 'No postive' to null values. Because we dont know what actual review is
reviews_df["review"] = reviews_df["review"].apply(lambda x: x.replace("No Negative", "").replace("No Positive", ""))
reviews_df.head()


# Cleaning review data by eliminating stopwords, punctuation.
# return the wordnet object value corresponding to the POS tag
def get_wordnet_pos(pos_tag):
    if pos_tag.startswith('J'):
        return wordnet.ADJ
    elif pos_tag.startswith('V'):
        return wordnet.VERB
    elif pos_tag.startswith('N'):
        return wordnet.NOUN
    elif pos_tag.startswith('R'):
        return wordnet.ADV
    else:
        return wordnet.NOUN

def clean_text(text):
    # lower text
    text = text.lower()
    # tokenize text and remove puncutation
    text = [word.strip(string.punctuation) for word in text.split(" ")]
    # remove words that contain numbers
    text = [word for word in text if not any(c.isdigit() for c in word)]
    # remove stop words
    stop = stopwords.words('english')
    text = [x for x in text if x not in stop]
    # remove empty tokens
    text = [t for t in text if len(t) > 0]
    # pos tag text
    pos_tags = pos_tag(text)
    # lemmatize text
    text = [WordNetLemmatizer().lemmatize(t[0], get_wordnet_pos(t[1])) for t in pos_tags]
    # remove words with only one letter
    text = [t for t in text if len(t) > 1]
    # join all
    text = " ".join(text)
    return(text)


# clean text data using above functions
reviews_df["review_clean"] = reviews_df["review"].apply(lambda x: clean_text(x))
reviews_df.head()


# wordcloud function
def show_wordcloud(data, title = None):
    wordcloud = WordCloud(
        background_color = 'white',
        max_words = 200,
        max_font_size = 40, 
        scale = 3,
        random_state = 42
    ).generate(str(data))

    fig = plt.figure(1, figsize = (20, 20))
    plt.axis('off')
    if title: 
        fig.suptitle(title, fontsize = 20)
        fig.subplots_adjust(top = 2.3)

    plt.imshow(wordcloud)
    plt.show()
    
# print wordcloud
show_wordcloud(reviews_df["review"])


# Visualize Word Frequency plot 
## custom function for ngram generation ##
def generate_ngrams(text, n_gram=1):
    token = [token for token in text.lower().split(" ") if token != "" if token not in stopwords.words('english')]
    ngrams = zip(*[token[i:] for i in range(n_gram)])
    return [" ".join(ngram) for ngram in ngrams]

## custom function for horizontal bar chart ##
def horizontal_bar_chart(df, color):
    trace = go.Bar(
        y=df["word"].values[::-1],
        x=df["wordcount"].values[::-1],
        showlegend=False,
        orientation = 'h',
        marker=dict(color=color))
    return trace


## Bar chart from +ve review ##
def frequent_word_plot(reviews_df, review, target ,ngram =1):
    """
    reviews_df = Dataset
    review = Text data or reviews column name
    Target = Label column name
    """
    train1_df = reviews_df[reviews_df[review]==1]
    train0_df = reviews_df[reviews_df[review]==0]
    freq_dict = defaultdict(int)
    for sent in train0_df[target]:
        for word in generate_ngrams(sent,ngram):
            freq_dict[word] += 1
    fd_sorted = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])
    fd_sorted.columns = ["word", "wordcount"]
    trace0 = horizontal_bar_chart(fd_sorted.head(50), 'blue')

    ## Get the bar chart from -ve review ##
    freq_dict = defaultdict(int)
    for sent in train1_df[target]:
        for word in generate_ngrams(sent,ngram):
            freq_dict[word] += 1
    fd_sorted = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1])[::-1])
    fd_sorted.columns = ["word", "wordcount"]
    trace1 = horizontal_bar_chart(fd_sorted.head(50), 'blue')

    # Creating two subplots
    fig = subplots.make_subplots(rows=1, cols=2, vertical_spacing=0.04,
                              subplot_titles=["Frequent words of positive review", 
                                              "Frequent words of negative review"])
    fig.append_trace(trace0, 1, 1)
    fig.append_trace(trace1, 1, 2)
    if ngram ==2:
        title="Bigram Count Plots"
    elif ngram==3:
        title="Trigram Count Plots"
    else:
        title="Word Count Plots"
    fig['layout'].update(height=1200, width=900, paper_bgcolor='rgb(233,233,233)', title=title)
    py.iplot(fig, filename='word-plots')


frequent_word_plot(reviews_df, 'is_bad_review', 'review' ,ngram =1)


frequent_word_plot(reviews_df, 'is_bad_review', 'review' ,ngram =2)


frequent_word_plot(reviews_df, 'is_bad_review', 'review' ,ngram =3)


# Distribution on train and test dataset
def EDA_distribution(train_df,test_df, review, target, plot= True):
    """
    train_df = Training dataset
    test_df = Testing dataset
    review = Text data or reviews
    Target = Label data
    """
    
    ## Number of words in the text ##
    train_df["num_words"] = train_df[review].apply(lambda x: len(str(x).split()))
    test_df["num_words"] = test_df[review].apply(lambda x: len(str(x).split()))

    ## Number of unique words in the text ##
    train_df["num_unique_words"] = train_df[review].apply(lambda x: len(set(str(x).split())))
    test_df["num_unique_words"] = test_df[review].apply(lambda x: len(set(str(x).split())))

    ## Number of characters in the text ##
    train_df["num_chars"] = train_df[review].apply(lambda x: len(str(x)))
    test_df["num_chars"] = test_df[review].apply(lambda x: len(str(x)))

    ## Number of stopwords in the text ##
    train_df["num_stopwords"] = train_df[review].apply(lambda x: len([w for w in str(x).lower().split() if w in stopwords.words('english')]))
    test_df["num_stopwords"] = test_df[review].apply(lambda x: len([w for w in str(x).lower().split() if w in stopwords.words('english')]))

    ## Number of punctuations in the text ##
    train_df["num_punctuations"] =train_df[review].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )
    test_df["num_punctuations"] =test_df[review].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )

    ## Number of title case words in the text ##
    train_df["num_words_upper"] = train_df[review].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))
    test_df["num_words_upper"] = test_df[review].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))

    ## Number of title case words in the text ##
    train_df["num_words_title"] = train_df[review].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))
    test_df["num_words_title"] = test_df[review].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))

    ## Average length of the words in the text ##
    train_df["mean_word_len"] = train_df[review].apply(lambda x: np.mean([len(w) for w in str(x).split()]))
    test_df["mean_word_len"] = test_df[review].apply(lambda x: np.mean([len(w) for w in str(x).split()]))


    ## Truncate some extreme values for better visuals ##
    train_df['num_words'].loc[train_df['num_words']>60] = 60 #truncation for better visuals
    train_df['num_punctuations'].loc[train_df['num_punctuations']>10] = 10 #truncation for better visuals
    train_df['num_chars'].loc[train_df['num_chars']>350] = 350 #truncation for better visuals

    f, axes = plt.subplots(3, 1, figsize=(10,20))
    sns.boxplot(x = target, y='num_words', data=train_df, ax=axes[0])
    axes[0].set_xlabel('Target', fontsize=12)
    axes[0].set_title("Number of words in each class", fontsize=15)

    sns.boxplot(x = target, y='num_chars', data=train_df, ax=axes[1])
    axes[1].set_xlabel('Target', fontsize=12)
    axes[1].set_title("Number of characters in each class", fontsize=15)

    sns.boxplot(x = target, y='num_punctuations', data=train_df, ax=axes[2])
    axes[2].set_xlabel('Target', fontsize=12)
    #plt.ylabel('Number of punctuations in text', fontsize=12)
    axes[2].set_title("Number of punctuations in each class", fontsize=15)
    plt.show()
    return train_df, test_df


# add sentiment anaylsis columns
sid = SentimentIntensityAnalyzer()
reviews_df["sentiments"] = reviews_df["review"].apply(lambda x: sid.polarity_scores(x))
reviews_df = pd.concat([reviews_df.drop(['sentiments'], axis=1), reviews_df['sentiments'].apply(pd.Series)], axis=1)


reviews_df.head()


df = reviews_df.copy()


# plot sentiment distribution for positive and negative reviews
for x in [0, 1]:
    subset = reviews_df[reviews_df['is_bad_review'] == x]
    
    # Draw the density plot
    if x == 0:
        label = "Good reviews"
    else:
        label = "Bad reviews"
    sns.distplot(subset['compound'], hist = False, label = label)


# add number of characters column
reviews_df["nb_chars"] = reviews_df["review"].apply(lambda x: len(x))
# add number of words column
reviews_df["nb_words"] = reviews_df["review"].apply(lambda x: len(x.split(" ")))
reviews_df.head()


# create doc2vec vector columns
from gensim.test.utils import common_texts
from gensim.models.doc2vec import Doc2Vec, TaggedDocument

documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(reviews_df["review_clean"].apply(lambda x: x.split(" ")))]

# train a Doc2Vec model with our text data
model = Doc2Vec(documents, vector_size=5, window=2, min_count=1, workers=4)

# transform each document into a vector data
doc2vec_df = reviews_df["review_clean"].apply(lambda x: model.infer_vector(x.split(" "))).apply(pd.Series)
doc2vec_df.columns = ["doc2vec_vector_" + str(x) for x in doc2vec_df.columns]
reviews_df = pd.concat([reviews_df, doc2vec_df], axis=1)
reviews_df.head()


# add tf-idfs columns
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(min_df = 10)
tfidf_result = tfidf.fit_transform(reviews_df["review_clean"]).toarray()
tfidf_df = pd.DataFrame(tfidf_result, columns = tfidf.get_feature_names())
tfidf_df.columns = ["word_" + str(x) for x in tfidf_df.columns]
tfidf_df.index = reviews_df.index
reviews_df = pd.concat([reviews_df, tfidf_df], axis=1)
reviews_df.head()


# show is_bad_review distribution
reviews_df["is_bad_review"].value_counts(normalize = True)

0    0.956761
1    0.043239
Name: is_bad_review, dtype: float64


# highest positive sentiment reviews (with more than 5 words)
reviews_df[reviews_df[ "nb_words"] >= 5].sort_values("pos", ascending = False)[["review", "pos"]].head(10)


# lowest negative sentiment reviews (with more than 5 words)
reviews_df[reviews_df["nb_words"] >= 5].sort_values("neg", ascending = False)[["review", "neg"]].head(10)


reviews_df


reviews_df.drop('is_bad_review',axis=1)


# feature selection and then training model
label = "is_bad_review"

# split the data into train and test
X_train, X_test, y_train, y_test = train_test_split(reviews_df.drop(label,axis=1), reviews_df[label], test_size = 0.20, random_state = 42)

ignore_cols = [label, "review", "review_clean"]
features = [c for c in reviews_df.columns if c not in ignore_cols]
X_train_new = X_train[features]
X_test_new = X_test[features]


# train a random forest classifier
rf = RandomForestClassifier(n_estimators = 100, random_state = 42)
rf.fit(X_train_new, y_train)

# show feature importance
feature_importances_df = pd.DataFrame({"feature": features, "importance": rf.feature_importances_}).sort_values("importance", ascending = False)
feature_importances_df.head(20)


# import eli5
# eli5.show_weights(model, vec=tfidf_vec, top=100, feature_filter=lambda x: x != '<BIAS>')


# ROC curve
def ROC_curve(df_train_x, df_train_y, df_test_x, df_test_y, model):
    y_pred_train = [x[1] for x in model.predict_proba(df_train_x)]
    y_pred_test = [x[1] for x in model.predict_proba(df_test_x)]
    fpr_train, tpr_train, _ = roc_curve(df_train_y, y_pred_train)
    fpr_test, tpr_test, _ = roc_curve(df_test_y, y_pred_test)
    
    roc_auc = auc(fpr_train, tpr_train)
    plt.figure(1, figsize = (10, 5))
    plt.subplot(1, 2, 1)
    lw = 2
    plt.plot(fpr_train, tpr_train, color='darkorange',
             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.0])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC - Train')
    plt.legend(loc="lower right")
    
    roc_auc = auc(fpr_test, tpr_test)
    plt.subplot(1, 2, 2)
    lw = 2
    plt.plot(fpr_test, tpr_test, color='darkorange',
             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.0])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC - Test')
    plt.legend(loc="lower right")
    
ROC_curve(X_train_new, y_train, X_test_new, y_test, rf)


def create_conf_mat(col,y_train,heading):
    def create_mat(test_class_set, predictions):
        """Function returns confusion matrix comparing two arrays"""
        if (len(test_class_set.shape) != len(predictions.shape) == 1):
            return print('Arrays entered are not 1-D.\nPlease enter the correctly sized sets.')
        elif (test_class_set.shape != predictions.shape):
            return print('Number of values inside the Arrays are not equal to each other.\nPlease make sure the array has the same number of instances.')
        else:
            # Set Metrics
            test_crosstb_comp = pd.crosstab(index = test_class_set,
                                            columns = predictions)
            # Changed for Future deprecation of as_matrix
            test_crosstb = test_crosstb_comp.values
            return test_crosstb
    
    for i, val in enumerate(col):
        conf_mat = create_mat(y_train, val)
        sns.heatmap(conf_mat, annot=True, fmt='d', cmap="YlGnBu", cbar=False)
        plt.xlabel('Predicted Values')
        plt.ylabel('Actual Values')
        plt.title(heading)

plt.figure(1, figsize = (10, 5))
plt.subplot(1, 2, 1)
pred_train_list = [rf.predict(X_train_new)]
create_conf_mat(pred_train_list, y_train, 'Training: Random forest Confusion Matrix')
   
plt.subplot(1, 2, 2)
pred_test_list = [rf.predict(X_test_new)]
create_conf_mat(pred_test_list, y_test, 'Testing: Random forest Confusion Matrix')


score_train = pd.DataFrame()
score_train['review'] = X_train['review']
score_train['label'] = y_train
score_train['RF_score'] = rf.predict_proba(X_train_new)[:, 1]
score_train['Record_category'] = 'Train'

score_test = pd.DataFrame()
score_test['review'] = X_test['review']
score_test['label'] = y_test
score_test['RF_score'] = rf.predict_proba(X_test_new)[:, 1]
score_test['Record_category'] = 'Test'

score = pd.concat([score_train, score_test]).reset_index(drop=True)
score.head()

	Hotel_Address	Additional_Number_of_Scoring	Review_Date	Average_Score	Hotel_Name	Reviewer_Nationality	Negative_Review	Review_Total_Negative_Word_Counts	Total_Number_of_Reviews	Positive_Review	Review_Total_Positive_Word_Counts	Total_Number_of_Reviews_Reviewer_Has_Given	Reviewer_Score	Tags	days_since_review	lat	lng
0	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	8/3/2017	7.7	Hotel Arena	Russia	I am so angry that i made this post available...	397	1403	Only the park outside of the hotel was beauti...	11	7	2.9	[' Leisure trip ', ' Couple ', ' Duplex Double...	0 days	52.360576	4.915968
1	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	8/3/2017	7.7	Hotel Arena	Ireland	No Negative	0	1403	No real complaints the hotel was great great ...	105	7	7.5	[' Leisure trip ', ' Couple ', ' Duplex Double...	0 days	52.360576	4.915968
2	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	7/31/2017	7.7	Hotel Arena	Australia	Rooms are nice but for elderly a bit difficul...	42	1403	Location was good and staff were ok It is cut...	21	9	7.1	[' Leisure trip ', ' Family with young childre...	3 days	52.360576	4.915968
3	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	7/31/2017	7.7	Hotel Arena	United Kingdom	My room was dirty and I was afraid to walk ba...	210	1403	Great location in nice surroundings the bar a...	26	1	3.8	[' Leisure trip ', ' Solo traveler ', ' Duplex...	3 days	52.360576	4.915968
4	s Gravesandestraat 55 Oost 1092 AA Amsterdam ...	194	7/24/2017	7.7	Hotel Arena	New Zealand	You When I booked with your company on line y...	140	1403	Amazing location and building Romantic setting	8	3	6.7	[' Leisure trip ', ' Couple ', ' Suite ', ' St...	10 days	52.360576	4.915968

	review	is_bad_review
0	I am so angry that i made this post available...	1
1	No Negative No real complaints the hotel was g...	0
2	Rooms are nice but for elderly a bit difficul...	0
3	My room was dirty and I was afraid to walk ba...	1
4	You When I booked with your company on line y...	0

	review	is_bad_review
488440	Would have appreciated a shop in the hotel th...	0
274649	No tissue paper box was present at the room	0
374688	Pillows Nice welcoming and service	0
404352	Everything including the nice upgrade The Hot...	0
451596	Lovely hotel v welcoming staff	0

	review	review_clean
488440	Would have appreciated a shop in the hotel th...	would appreciate shop hotel sell drinking wate...
274649	No tissue paper box was present at the room	tissue paper box present room
374688	Pillows Nice welcoming and service	pillow nice welcome service
404352	Everything including the nice upgrade The Hot...	everything include nice upgrade hotel revamp s...
451596	Lovely hotel v welcoming staff	lovely hotel welcome staff

	review	review_clean	neg	neu	pos	compound
488440	Would have appreciated a shop in the hotel th...	would appreciate shop hotel sell drinking wate...	0.049	0.617	0.334	0.9924
274649	No tissue paper box was present at the room	tissue paper box present room	0.216	0.784	0.000	-0.2960
374688	Pillows Nice welcoming and service	pillow nice welcome service	0.000	0.345	0.655	0.6908
404352	Everything including the nice upgrade The Hot...	everything include nice upgrade hotel revamp s...	0.000	0.621	0.379	0.9153
451596	Lovely hotel v welcoming staff	lovely hotel welcome staff	0.000	0.230	0.770	0.7717

	review	pos
43101	A perfect location comfortable great value	0.931
211742	Clean comfortable lovely staff	0.907
175551	Friendly welcome Comfortable room	0.905
365085	Good location great value	0.904
109564	Clean friendly and comfortable	0.902
145743	Good value amazing location	0.901
407590	breakfast excellent Clean comfort	0.899
407546	Great place I enjoyed	0.881
218571	Beautiful Quirky Comfortable	0.878
128308	Super friendly staff	0.877

	review	neg
193086	No dislikes LOCATION	0.831
356368	Nothing Great helpful wonderful staff	0.812
318516	A disaster Nothing	0.804
458794	Nothing Excellent friendly helpful staff	0.799
29666	A bit noisy No	0.796
426057	Dirty hotel Smells bad	0.762
263187	Very bad service No	0.758
443796	Nothing perfect	0.750
181508	Window blind was broken	0.744
175316	Nothing Super friendly staff	0.743

	feature	importance
3	compound	0.036573
2	pos	0.025650
0	neg	0.022572
9	doc2vec_vector_3	0.020444
10	doc2vec_vector_4	0.020151
6	doc2vec_vector_0	0.018400
8	doc2vec_vector_2	0.017256
7	doc2vec_vector_1	0.017011
4	nb_chars	0.016774
1	neu	0.014827
5	nb_words	0.014098
950	word_dirty	0.010021
285	word_bad	0.009757
2853	word_room	0.009346
2239	word_nothing	0.008959
1639	word_hotel	0.006824
1945	word_location	0.006715
3202	word_staff	0.006587
2284	word_old	0.006283
3216	word_star	0.005911

	review	RF_score	Record_category
0	However there was no complimentary wifi in th...	0.00	Train
1	Bathroom has a slightly damp smell but was ot...	0.01	Train
2	Nothing Everything	0.00	Train
3	No coffee in the room the shower smelled and ...	0.02	Train
4	The room was a bit small for the price and th...	0.01	Train

	review	is_bad_review	review_clean	neg	neu	pos	compound	nb_chars	nb_words	doc2vec_vector_0	...	word_yet	word_yoghurt	word_yogurt	word_young	word_yr	word_yummy	word_zero	word_ziggo	word_zone	word_zuid
488440	Would have appreciated a shop in the hotel th...	0	would appreciate shop hotel sell drinking wate...	0.049	0.617	0.334	0.9924	599	113	0.208503	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
274649	No tissue paper box was present at the room	0	tissue paper box present room	0.216	0.784	0.000	-0.2960	44	10	0.049081	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
374688	Pillows Nice welcoming and service	0	pillow nice welcome service	0.000	0.345	0.655	0.6908	36	7	-0.060760	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
404352	Everything including the nice upgrade The Hot...	0	everything include nice upgrade hotel revamp s...	0.000	0.621	0.379	0.9153	155	27	0.034012	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
451596	Lovely hotel v welcoming staff	0	lovely hotel welcome staff	0.000	0.230	0.770	0.7717	32	7	0.007261	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
274862	Bathroom water easy made the bathroom wet whe...	0	bathroom water easy make bathroom wet bath wal...	0.000	0.614	0.386	0.8834	142	25	-0.025032	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
9732	Room very small chair tatty in the room	1	room small chair tatty room	0.000	1.000	0.000	0.0000	41	10	0.043768	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
424201	Expensive rates and mini bar prices Roof top ...	0	expensive rate mini bar price roof top pool vi...	0.000	0.886	0.114	0.2023	80	16	0.170338	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
72380	There was a very loud AC machine right outsid...	0	loud ac machine right outside window affect sl...	0.047	0.845	0.108	0.4767	176	36	0.305109	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
273809	This is definitely not 4 stars hotel Location...	0	definitely stars hotel location good hotel req...	0.000	0.647	0.353	0.7684	106	20	-0.025718	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	review	review_clean	neg	neu	pos	compound	nb_chars	nb_words	doc2vec_vector_0	doc2vec_vector_1	...	word_yet	word_yoghurt	word_yogurt	word_young	word_yr	word_yummy	word_zero	word_ziggo	word_zone	word_zuid
488440	Would have appreciated a shop in the hotel th...	would appreciate shop hotel sell drinking wate...	0.049	0.617	0.334	0.9924	599	113	0.208503	0.143090	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
274649	No tissue paper box was present at the room	tissue paper box present room	0.216	0.784	0.000	-0.2960	44	10	0.049081	0.173981	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
374688	Pillows Nice welcoming and service	pillow nice welcome service	0.000	0.345	0.655	0.6908	36	7	-0.060760	0.127072	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
404352	Everything including the nice upgrade The Hot...	everything include nice upgrade hotel revamp s...	0.000	0.621	0.379	0.9153	155	27	0.034012	0.179485	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
451596	Lovely hotel v welcoming staff	lovely hotel welcome staff	0.000	0.230	0.770	0.7717	32	7	0.007261	0.124126	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
274862	Bathroom water easy made the bathroom wet whe...	bathroom water easy make bathroom wet bath wal...	0.000	0.614	0.386	0.8834	142	25	-0.025032	-0.273785	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
9732	Room very small chair tatty in the room	room small chair tatty room	0.000	1.000	0.000	0.0000	41	10	0.043768	0.137481	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
424201	Expensive rates and mini bar prices Roof top ...	expensive rate mini bar price roof top pool vi...	0.000	0.886	0.114	0.2023	80	16	0.170338	0.095394	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
72380	There was a very loud AC machine right outsid...	loud ac machine right outside window affect sl...	0.047	0.845	0.108	0.4767	176	36	0.305109	-0.095217	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
273809	This is definitely not 4 stars hotel Location...	definitely stars hotel location good hotel req...	0.000	0.647	0.353	0.7684	106	20	-0.025718	0.121640	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

SentimentAnalyzer is a tool to implement and facilitate Sentiment Analysis tasks using NLTK features and classifiers.¶